Department of Biomedical Informatics

It’s similar to the “precogs” who predict crime in the movie Minority Report, but for sepsis, the deadly response to infection. That’s how Tim Buchman, director of the Emory Critical Care Center, described an emerging effort to detect and ward off sepsis in ICU patients hours before it starts to make their vital signs go haywire.

As landmark clinical studies have documented, every hour of delay in giving someone with sepsis antibiotics increases their risk of mortality. So detecting sepsis as early as possible could save lives. Many hospitals have developed “sniffer” systems that monitor patients for sepsis risk. See our 2016 feature in Emory Medicine for more details.

What Shamim Nemati and his colleagues, including bioinformatics chair Gari Clifford, have been exploring is more sophisticated. A vastly simplified way to summarize it is: if someone has a disorderly heart rate and blood pressure, those changes can be an early indicator of sepsis.* It requires continuous monitoring – not just once an hour. But in the ICU, this can be done. The algorithm uses 65 indicators, such as respiration, temperature, and oxygen levels — not only heart rate and blood pressure. See below.

Example patient graph. Green = SOFA score. Purple = Artificial Intelligence Sepsis Expert (AISE) score. Red = official definition of sepsis. Blue = antibiotics. Black + red = cultures. Around 4 pm on December 20, roughly 8 hr prior to any change in the SOFA score, the AISE score starts to increase. The top contributing factors were slight changes in heart rate, respiration, and temperature, given that the patient had surgery in the past 12hr with a contaminated wound and was on a mechanical ventilator. Close to midnight on December 21, other factors show abnormal changes. Five hours later, the patient met the Sepsis-3 definition of sepsis.

As recently published in the journal Critical Care Medicine, Nemati’s algorithm can predict sepsis onset – with some false alarms – 4, 8 even 12 hours ahead of time. No predictor is going to be perfect, Nemati says. The paper lays out specificity, sensitivity and accuracy under various timelines. They get to an AUROC (area under receiving operating characteristic) performance of 0.83 to 0.85, which this explainer web site rates as good (B), and is better than any other previous sepsis predictor. Read more

Imagine someone undergoing treatment by a psychiatrist. How do we know the treatment is really working or should be modified?

To assess whether the patient’s condition is objectively improving, the doctor could ask him or her to take home a heart rate monitor and wear it continuously for 24 hours. An app connected to the monitor could then track how much the patient’s heart rate varies over time and how much the patient moves.

Heart rate variability can be used to monitor psychiatric disorders

MD/PhD student Erik Reinertsen is the first author on two papers in Physiological Measurement advancing this approach, working under the supervision of Gari Clifford, interim chair of Emory’s Department of Biomedical Informatics.

Clifford’s team has been evaluating heart rate variability and activity as a tool for monitoring both PTSD (post-traumatic stress disorder) and schizophrenia. Clifford says his team’s research is expanding to look at treatment-resistant depression and other mental health issues.

For clinical applications, Clifford emphasizes that his plans focus on tracking disease severity for patients who are already diagnosed, rather than screening for new diagnoses. His team is involved in much larger studies in which heart rate data is being combined with physical activity data from smart watches, body patches, and clinical questionnaires, as well as other behavioral and exposure data collected through smartphone usage patterns.

Intuitively, heart rate variability makes sense for monitoring PTSD, because one of the core symptoms is hyperarousal, along with flashbacks and avoidance or numbness. However, it turns out that the time that provides the most information is when heart rate is lowest and study participants are most likely asleep, or at their lowest ebb during the night.

Home sleep tests generate a ton of information, which can be mined. This approach also fits into a trend for wearable medical technology, recently highlighted in STAT by Max Blau (subscription needed).

The research on PTSD monitoring grows out of work by cardiologists Amit Shah and Viola Vaccarino on heart rate variability in PTSD-discordant twin veterans (2013 Biological Psychiatry paper). Shah and Vaccarino had found that low frequency heart rate variability is much less (49 percent less) in the twin with PTSD. Genetics influences heart rate variability quite a bit, so studying twins allows those factors to be accounted for. Read more